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2017

2017

Linear regression is one of the most prevalent techniques in machine learning, however, it is also common to use linear… Expand

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2010

Highly Cited

2010

Data envelopment analysis (DEA) is known as a nonparametric mathematical programming approach to productive efficiency analysis… Expand

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Highly Cited

2009

Highly Cited

2009

The paper examines second stage DEA efficiency analyses, within the context of a censoring data generating process (DGP) and a… Expand

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Highly Cited

2007

Highly Cited

2007

Totla least squares (TLS) is a method of fitting that is appropriate when there are errors in both the observation vector $b (mxl… Expand

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Highly Cited

2007

Highly Cited

2007

We propose a method of least squares approximation (LSA) for unified yet simple LASSO estimation. Our general theoretical… Expand

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Highly Cited

2006

Highly Cited

2006

This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear probability model and provides… Expand

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2005

Highly Cited

2005

Harmonic estimation for a signal distorted with additive noise has been an area of interest for researchers in many disciplines… Expand

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Highly Cited

1990

Highly Cited

1990

This paper derives the asymptotic distribution for a vector of sample autocorrelations of regression residuals from a quite… Expand

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Highly Cited

1990

Highly Cited

1990

Continuum regression: Cross-validated sequentially constructed prediction embracing ordinary least s

[Read before The Royal Statistical Society at a meeting organized by the Research Section on Wednesday, October 25th, 1989… Expand

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1987

Highly Cited

1987

Time series variables that stochastically trend together form a cointegrated system. OLS and NLS estimators of the parameters of… Expand

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